Zero-shot or few-shot named entity recognition is popular for low resource languages or domains. [1]} proposed a label embedding method for the zero-shot NER task. They used prototypical and hierarchical information to learn the label embeddings and adapted a zero-shot framework for the NER task. [2]} proposed a semi-supervised learning technique to tackle the NER task using a prototypical network in a few-shot setting. It learns intermediate representations of words that cluster well into named entity classes. This property of the model allows classifying words with a limited number of training instances, and can potentially be used as a zero-shot learning method. [3]} proposed an unsupervised cross-lingual NER model that can transfer knowledge from one language to another without using any annotated data or parallel corpora.
